4DVAR Optimization & Use-cases for Deep Learning in Earth

4DVAR Optimization & Use-cases for Deep

Learning in Earth Sciences

BoM R&D Workshop, 9th

December 2016

Dr. Phil Brown

Earth Sciences Segment Leader

Topics

● Optimization of UM 4DVAR

● Courtesy of Lucian Anton

● Use-Cases for Deep Learning in Earth Sciences

Copyright 2016 Cray Inc. - BoM R&D Workshop2

Topics

● Future Technology Trends

● Challenges & Opportunities for Data Assimilation

● Use-Cases for Deep Learning in Earth Sciences


4

Historical Performance Trends


WRF Data from SPEC-FP-2006-rate:

https://www.spec.org/cgi-bin/osgresults?conf=rfp2006

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Nehalem-EP Westmere-EP Sandy Bridge-EP

Ivy Bridge-EP Haswell-EP Broadwell-EP

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WRF Performance

FLOPs aren’t the bottleneck!




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WRF Performance Peak FLOPS

Memory Bandwidth & Serial Performance




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WRF Performance Memory Bandwidth Serial Performance

Exascale Computing Memory Trends


CPU

Memory

(DRAM)

Storage

(HDD)

CPU

Near Memory

(HBM/HMC)

Near Storage

(SSD)

Far Memory

(DRAM/NVDIMM)

Far Storage

(HDD)

On Node

Off Node

On Node

Off Node

Today Future



CPU

Memory

(DRAM)

Storage

(HDD)

CPU

Near Memory

(HBM/HMC)

Near Storage

(SSD)

Far Memory

(DRAM/NVDIMM)

Far Storage

(HDD)

On Node

Off Node

On Node

Off Node

Today Future

Solid-state “Near” Storage

● SSDs enable very high bandwidth storage close to compute● 1TB/s per PB capacity

● Configured as a “consumable” resource

● Use-cases:● Shared Scratch● Workflows

● Checkpoint-Restart




CPU

Memory

(DRAM)

Storage

(HDD)

CPU

Near Memory

(HBM/HMC)

Near Storage

(SSD)

Far Memory

(DRAM/NVDIMM)

Far Storage

(HDD)

On Node

Off Node

On Node

Off Node

Today Future

Non-volatile Memory

● New non-volatile memories on the horizon● 3D Xpoint, RRAM etc.

● Block and/or byte addressable● Somewhat slower than DRAM

● Opportunity for multi-TB node memory?● Need a really compelling use-case to justify on every node

● Software layers/interfaces still unclear/in development● User controlled (either as memory, or as storage)● Memory expansion (fronted by RAM cache)

● Distributed resilient SAN/filesystems?




CPU

Memory

(DRAM)

Storage

(HDD)

CPU

Near Memory

(HBM/HMC)

Near Storage

(SSD)

Far Memory

(DRAM/NVDIMM)

Far Storage

(HDD)

On Node

Off Node

On Node

Off Node

Today Future

Next-Generation Memory Technologies


● Benefits: ● Higher Memory Bandwidth

● Lower Power Consumption per GB/s

● Higher density?

● Downsides: ● Lower Primary Memory Capacity● More Complicated Memory

Hierarchy?

http://www.amd.com/en-us/innovations/software-technologies/hbm https://software.intel.com/en-us/articles/what-disclosures-has-intel-made-about-knights-landing

https://devblogs.nvidia.com/parallelforall/nvlink-pascal-stacked-memory-feeding-appetite-big-data/

Implications for Data Assimilation

● Parallelism is here to stay

● Bad news for classic 4DVAR?

● Low resolution linear/adjoint models

● EnVAR should help

● Use-cases for large non-volatile memories?

● Primary memory may get smaller but much faster


What is Machine/Deep Learning?

● Deep Learning used to describe a family of algorithms related to multi-level neural networks:● Deep Neural Networks

● Convolutional Neural Networks

● Recurrent Neural Networks

● Lots more!

● Key enabler has been access to compute resources● DL is predominantly FLOP bound

● Large scale problems rapidly becoming “HPC”-class

● Delivering “state of the art” results in computer vision, speech recognition, natural language processing etc.


Opportunities for Machine/Deep Learning in Weather/Climate

● Almost the opposite of a physics/dynamics based model● Arduous to train, but comparatively quick to run

● Data producer vs data consumer

● Use-cases will be complementary?

● Some ideas:● Rapid classifiers for radar/observations

● Optimal observation selection● Alternative approaches for parameterization● Pattern recognition in model outputs

● Infilling/smoothing model outputs


Thank you for your attention

19

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Copyright 2016 Cray Inc. - BoM R&D Workshop

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4DVAR Optimization & Use-cases for Deep Learning in Earth